The AI Culture in Agriculture

Sudheer Devalla

Technology has redefined agriculture over the years and the continuous advancements impacted global agriculture and farmers in many ways. Agriculture is a mainstream occupation in many countries across the globe and with the rising population (which as per UN projections will increase from 7.5 billion to 9.7 billion in 2050), there will be tremendous pressure on land, as there will be an extra 4% of land which will come under cultivation by 2050. This will put pressure on our farmers and governments to do more with less. Technological advancements with increased adoption will enable farmers to be more productive and profitable with the same land area.

The most heard technology in recent years is AI (Artificial Intelligence). These advanced AI models address the most complex pain points of farmers with simple solutions. The use cases of the AI application in agriculture are unlimited. AI, machine learning and IoT sensors, which can provide real-time data, can improve agricultural efficiencies by intervening in almost every stage of cultivation processes, hence contributing to improving yields and reduced food production costs.

Here are some of the key interventions of AI in agriculture:

Predictive insights

As per reports, the number of data points gathered on an average farm will increase from 1,90,000 to 4.1 million in 2050. Using abundant data points helps in farming with precision methods. The data coupled with AI models can help in understanding minute details like the best time to sow. This might be a micro input to the farmer, yet it decides the yield and profitability. These models can help in understanding soil health and fertilizer recommendations and their usage. Crop yield predictions and price forecasts are very crucial inputs for farmers globally. Unpredictable climate changes and fluctuations in prices create uncertainty for farmers. Companies are using satellite imagery and weather data to assess the acreage and monitor crop health on a real-time basis. With the help of AI models coupled with big data, this can help in the prediction of pests and diseases more proactively. Yield predictions and demand planning can help farmers and governments map the supply and demand of the crop produce. AI models can help in predicting human resource planning for minimizing costs in labor-intensive tasks.

Crop and soil monitoring

Traditionally, soil health and crop health were left to human judgment, and this has its limitations and inaccuracies. Micro and macro nutrients in the soil are very critical for the health of the crop and have a direct impact on the quality and quantity of yield. In general, soil samples are brought to the lab and are analyzed to understand the soil health. Some imagery-based AI models proved to understand the sand content and SOM (Soil Organic Matter) estimates with almost lab accuracy. And once the crop is in the soil, monitoring the stages of the growth of the crop at various stages is very critical. Understanding different environmental conditions at each stage of crop growth are vital and this will help in adjustments to protect the crop's health resulting in better yield. Advanced AI models can help in understanding both soil and crop conditions based on satellite or drone imagery accompanied by various data points and statistical models.

Automated irrigation

Water is one of the major inputs of agriculture that plays an important role in the development of the crop. The new AI models that support decision-making have now set water management on a new trajectory. These models coupled with IoT and big data can help in analyzing the crop stress due to scarce or excess water supply, as both these conditions are fatal to the crop produce. Ai models use statistical methods using critical factors like crop type, species, climatic data, temperature, soil moisture, soil type and source of irrigation to predict the water requirements. By using these AI models, we can also eliminate the human error of approximation. Advanced AI automated irrigation systems can help in understanding and providing water precisely. This helps in saving water and results in a high yield. These systems can help in saving time and money, and increase productivity.

Pest and disease prediction and detection

Crop growth in various climatic conditions are prone to various pests and diseases at every stage. In the ever-changing global climatic conditions, it is a very challenging task to predict the occurrence of pests and diseases. AI models coupled with IoT devices and sensors can help in getting real-time microclimatic data such as soil moisture, humidity, temperature etc. and are used by AI models to understand the favourable conditions for pest infestation and disease occurrence. Such advanced AI models can predict pests and diseases with the greatest accuracy, at least 15 to 20 days prior. This prediction can help the farmer to be equipped with all necessary precautions and minimize the risk of losing the yield.

In many cases of post occurrence of pests and diseases, it is a challenge to identify the stage and type of pest and diseases which affected the crop. Image-trained AI models can detect the stages and types of pests and diseases with the greatest accuracy and confidence. This detection can also help farmers to understand the precise health conditions of the crop and take necessary proactive and reactive measures.

Intelligent spraying

It is not just identifying the affected crops and spotting disorders in the crop, but also about preventing or mitigating the spread of damage. UAVs (Unmanned Aerial Vehicles) equipped with AI vision and imagery modules can help in automating the spraying of pesticides and fertilizers uniformly and precisely across the field. This technology helps in identifying the target areas and calculating the amount of chemicals to be sprayed, and execute the spraying with highest accuracy. This precision intelligent spraying helps reduce the risk of contaminating crops, humans, animals and water resources. These Ai models can help in identifying weeds and sprays with such accuracy that it mitigates the collateral damage to healthy crops.

Automatic weeding

Even though weed identification with intelligent spraying works quite efficiently, vision models with machine learning can help in building robots to perform automatic weeding. The robots are trained to distinguish between weeds and crops and eliminate them with the greatest precision and are not just highly productive but also environment friendly. By doing this, we are eliminating the usage of herbicides and thus making the whole operation more efficient and productive.

Smart harvesting

Understanding the growth stages of crop production is the greatest challenge, especially in highly perishable crops like a tomato. Observing and estimating the growth and maturity of a crop is highly labour-intensive work and economically inefficient for farmers. With image-based trained AI models, farmers can understand the maturity of crop production with the highest accuracy and can forecast and equip for efficient harvesting.

Produce grading and sorting

The AI vision modules can help farmers even once the crops have been harvested. As these models are instrumental in understanding soil health, crop health, pests and diseases, they can also identify and sort quality produce and separate it from defective ones. AI vision models can inspect fruits and vegetables for size, shape, colour and volume, and can automate the sorting and grading process with greater accuracy compared to trained professionals. This will help farmers and traders to save money, and minimize human efforts and human errors in the sorting and grading process.

In conclusion, AI coupled with big data and IoT can help all the stakeholders in the agriculture sector including farmers, traders and governments to operate most efficiently and productively. This can also minimize and mitigate all possible risks while reducing losses and increasing the yield.

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